Tag: bigquery

ListenBrainz is a project is that has the potential to gather a lot of data quickly, which would require us to have a Big Data infrastructure, which can be expensive. In an effort to use our available cash wisely, we began to look around for ways to take advantage of other infrastructures with lower costs.

Two years ago at the Google Summer of Code mentor summit I met with a representative from the BigQuery team who said that Google was happy to host any public data set for free! I immediately took them up on this offer and started a conversation. With much time passed, we finally managed to get the data set live!

You’ll need a Google account to log in with — once you’re logged in, every user gets 5TB of query traffic free per month. That is quite a lot for how large this dataset is currently. The schema for this table is defined here and what the data elements mean are defined in our API docs. To get you started, I’ve written a few sample queries:

BigQuery uses an SQL like syntax, so if you know some SQL then diving right in should be easy. The queries above should give you an idea of what you can do with this data. Now, please know that currently we have approaching 30M listens, so the dataset is still quite small. We’re very much interested to see what sort of things people can come up with in the near future.

Finally, some notes about openness and proprietary software: Given that we have limited resources, we aim to make the most things happen with the services that are at our disposal. Google has been extremely generous to us over the years and we’re very pleased to have access to BigQuery now.

That is not to say that we’re putting all of our eggs in one basket or forcing people to use BigQuery. Our InfluxDB database hosted on our own servers keeps the master archival copy of our listen data. Soon we hope to make dumps of this data available for anyone to download and play with using whatever tools they would like. With this setup we are not fully reliant on Google for keeping this project alive. We’re glad to have their support, but should circumstance change, we can find another BigData solution and load our master archival copy there.

Now, go play with this very promising data and post some of your favorite queries in the comments!